Cloud + Deep Reinforcement Learning + Microsim: the Future of Mixed Autonomy Traffic

The question of how will self-driving cars will change urban mobility patterns is an open topic today. This talk describes scientific contributions in the field of reinforcement learning presented in the context of enabling mixed-autonomy mobility, the gradual and complex integration of automated vehicles into the existing traffic system. The talk explores the potential impact of a small fraction of automated vehicles on low-level traffic flow dynamics, using novel techniques in model-free deep reinforcement learning. Illustrative examples will be presented in the context of a new open-source computational platform called FLOW, which integrates state of the art microsimulation tools with deep-RL libraries on AWS EC2. Interesting behavior of mixed autonomy traffic will be revealed in the context of emergent behavior of traffic.

Speaker: Alexandre Bayen, UC Berkeley